Studien- und Masterarbeiten bei SCS

Wenn Sie Elektrotechnik, Informatik, Physik oder Mathematik studieren und zudem neugierig, lernfähig, kommunikativ und engagiert sind, dann haben Sie bei SCS die Möglichkeit, Ihre Studien-/ Masterarbeit zu realisieren. Die Arbeit wird im Normalfall zu zweit, in Ausnahmefällen von einer Person verfasst. Sie soll sowohl eine Forschungs- als auch eine Entwicklungskomponente enthalten.

Wenn Sie eine eigene Idee für Ihre Studien-/Masterarbeiten und die Unterstützung Ihres Professors haben, melden Sie sich bei uns. Ansonsten lassen Sie sich von den nachfolgenden Ideen inspirieren. Wir helfen gerne bei der Umsetzung.

Mögliche Studien- und Masterarbeiten

  • Developing a Payed Web Service that Guarantees no Data Collection Developing a Payed Web Service that Guarantees no Data CollectionWhat happens with our data when you log-in to an online service, e. g. over a smartphone app? Is your complete usage profile forwarded to the company? Many online services, e. g. online newspapers, have a subscription model, where the user has to pay for its usage. It is valid to restrict access to paying customers, but is it necessary for a newspaper to know exactly what articles were read by which customers? The goal of this thesis is to evaluate an approach that obfuscates the actual usage of a service, while maintaining the possibility to restrict access to paying customers.
  • Privacy Preserving OAuth Service with TEEs Privacy Preserving OAuth Service with TEEsIf we use Google, Facebook, or SwissID logins for third party sites, these login providers get to know when we log into which service. Moreover, the user has only little control over which data from her/his Google or Facebook accounts are shared with the service she/he's logging into. The goal of this thesis is to build a blind OAuth service, which can be easily integrated into third party sites just like the above mentioned services.
  • Enhanced Body Control through Body Tracking and Data Visualization Enhanced Body Control through Body Tracking and Data VisualizationCurrently, especially athletes and tech geeks are using tools that allow to measure and track body functions like pulse, blood pressure, or respiration. However, the trend towards measurement of bodily functions is increasing steadily, while the possibilities of data acquisition are constantly being extended. The goal of this work is to develop a new tool that allows a human to learn to control non-visible body functions through visual feedback provided by the tool. This involves selecting a number of matching measuring devices, finding and creating suitable visualizations for the generated data in real-time, and exploring the potential of this newly created tool.
  • Deep Learning for Estimating the Rail-Wheel Forces of an ICN based on Track Geometry Deep Learning for Estimating the Rail-Wheel Forces of an ICN based on Track GeometryTilting trains can travel at higher speeds without degrading the passenger comfort. However, these higher speeds lead to larger rail-wheel interaction forces. This larger forces have to be monitored in order to maintain general safety (e.g. to prevent derailment). The monitoring can be done with specially equipped passenger trains (On Board Monitoring "OBM", as it is the case with the ICN types of trains), or with measurement trains like the VT612. These procedures are very cost-intense. Additionally, if limit violations are observed, it is often difficult to figure out what caused this effect.
  • Automating DDH Diagnosis using Machine/Deep Learning Techniques Automating DDH Diagnosis using Machine/Deep Learning TechniquesBetween 2 and 3 percent of all infants are diagnosed with developmental dysplasia of the hip (DDH), and it is believed that approximately 30% of all hip replacement surgeries on patients below the age of 60 are owed to DDH. Graf's Method - the state of the art for diagnosis of DDH in infants - can aid to initiate early on, non-invasive treatment of infants with very high success rates.
  • Jogging with Acoustic Feedback based on Body Rhythms Jogging with Acoustic Feedback based on Body RhythmsJogging has become very popular in recent years, with millions of people across the world integrating it into their regular exercise regime. Many, however, do so with poor movement coordination, particularly in terms of the synchronization of body rhythms such as cadence, breathing and heartbeats. In this Master’s thesis, we aim to develop a digital application that will support runners in finding their natural jogging ‘groove’ by providing them with acoustic, real-time feedback on their individual style and technique.
  • Generating 3D Indoor Maps Autonomously Through Reiforcement Learning Generating 3D Indoor Maps Autonomously Through Reiforcement LearningIn September 2017, Unity Technologies released the first open beta of the Unity Machine Learning Agents Toolkit. With this toolkit, it is possible to train agents (e.g. through reinforcement learning) to solve a specific task in a simulated environment.
  • MLOps: Bring Your Model to Production MLOps: Bring Your Model to ProductionThe field of ML is growing up. In the past, questions like "which AI algorithm should I choose" and "what is the best way to train it" were the main focus of AI projects. Today, these topics are well explored. Startups and big cloud providers offer services to ease the first steps and usage of AI. In addition, we can use publicly available, pre-trained models and use transfer learning to get a sound predictive performance with less effort than building our own model from scratch. Now, new questions get into focus: how can we bring our model actually to production in a reliable and reproducible way? How can we integrate data acquisition, training, and monitoring of our model into an automated system?
  • Deep Learning in the Wild Deep Learning in the WildClimate change and human exploitation of our planet has a significant influence on the habitat and existence of wild animals. The resulting biodiversity loss threatens ecosystems and the human development that depends on them. Protecting these habitats is based on delivering evidence by collecting data. This is usually labour intensive, since it depends on field work done by biologist and volunteers. This master thesis tries to make a contribution to scale up this important process by using acoustic detection of animals using deep learning on embedded systems.
  • Building a new Ecological and Private Cryptocurrency Building a new Ecological and Private Cryptocurrencyencointer proposes a new blockchain-based cryptocurrency with an ecological consensus mechanism using trusted execution environments and an egalitarian money supply policy, where money issuance is done by individuals attending randomized pseudonym key signing events. encointer also features scalable private transactions and trustless off-chain smart contracts. This thesis shall build an encointer testnet based on Hyperledger Sawtooth. Depending on the student’s preference, emphasis ...
  • Automatisierte Anamnese von Heizungen und Gebäudeenergieanlagen Automatisierte Anamnese von Heizungen und GebäudeenergieanlagenIn der Schweiz besteht ein grosses Energieeffizienzpotential in Gebäuden. Bestehende Heizungsanlagen in Gebäuden sind heute oft nicht ideal eingestellt, weisen Mängel auf oder sind gar dysfunktional. Zur Detektion dieser Mängel soll ein Prototyp eines automatisierten Anamnese-Systems aufgebaut werden, welches aufgrund von sensorischen Messgrössen (primär Temperaturfühler) die Anlage erstens identifiziert, zweitens auf Fehler analysiert und drittens ...
  • Detektion von Kabelfehlern in der Aussenanlage Detektion von Kabelfehlern in der AussenanlageEine Herausforderung beim Betrieb von elektrifizierten Bahnstrecken ist die Überwachung von Elementen (z.B. Weichen) und deren Verkabelung in der Aussenanlage. Ein weit verbreitetes Verfahren ist die Überwachung der Adern durch einen Ruhestrom. Speziell bei den heute immer noch weit verbreiteten Lösungen auf Relaisbasis ist die Überwachung des Stromes sehr eingeschränkt. Kurzschlüsse und der Einfluss von Fremdspannungen bleiben daher oft unerkannt.
  • Flexible FPGA-based Test Equipment to Model and Characterize the Real-Time Behavior of an Ethernet Ring Flexible FPGA-based Test Equipment to Model and Characterize the Real-Time Behavior of an Ethernet RingIn this work, flexible test equipment for Ethernet network hardware and firmware shall be evaluate and commissioned. The goal is to model and characterize an Ethernet network based on BroadR-Reach with ring topology and HSR protocol for its real-time behavior.The test equipment could be based on devices available at netfpga.org. Extensions may be developed to further improve functionality.
  • Software-Architecture for SDR Software-Architecture for SDR
    Alle aktuellen SDR Softwarepakete haben Einschränkungen. Es gibt GNU Radio, welches kompliziert und schwierig zu bedienen ist, und es gibt verschiedene SDR GUIs, welche jedoch nicht einfach erweiterbar sind. Das Ziel ist eine einfach zu bedienende Software zu erstellen, welche es ermöglicht, Radiosignale zu analysieren und Algorithmen auszuprobieren. Fertige Abläufe sollen dann auf Knopfdruck in eine C-Datei exportiert werden können, um eine Integration in Embedded Systeme so leicht wie möglich zu gestalten.
  • Virtual Reality as 3D Ground Truth Generator for AI, Machine Learning and Deep Learning Virtual Reality as 3D Ground Truth Generator for AI, Machine Learning and Deep LearningTraining effective Artificial Intelligence (AI) algorithms today often requires large amounts of ground truth data. Typically, this is a laborious, costly and time-consuming process often requiring manual adjustment. These problems can be overcome by combining AI with Virtual Reality (VR): an emerging technology with various applications in medicine, training, business and simulation. VR creates artificial environments that often resemble our real world.This project aims at exploring the potential of VR as 3D ground truth generator for state-of-the-art Machine Learning (ML) and Deep Learning (DL) Algorithms.
  • Transfer Learning for Image Segmentation using Convolutional Neural Networks Transfer Learning for Image Segmentation using Convolutional Neural NetworksToday’s Machine Learning (ML) success is often limited by a lack of labelled ground truth (GT) data to train the models. This is especially true for applications in medical imaging.Transfer learning (TL) is a state-of-art ML-technique that can be useful to overcome this problem for similar yet distinct tasks. TL tries to apply knowledge or models gained from a task to a second one. In the field of image recognition, TL-methods for deep neural nets re-use parameters trained on source domain data except for the output layer (see image on the right).In medical optical coherence tomography (OCT) of the eye only a limited set of GT-labeled images exists. In addition, there are several device manufacturers and imaging approaches. Thus, transfer learning could 1) enable an easier addition of new device types or imaging techniques, 2) improve the quality of available segmentations even with limited data.
  • Towards User-independent 2D / 3D Object Classification of Complex Life Science Images Towards User-independent 2D / 3D Object Classification of Complex Life Science ImagesWe develop novel machine vision & mathematical morphology algorithms to analyze complex multi-modal Life Science images.
  • Self-Calibration for Embedded Stereo Vision System Self-Calibration for Embedded Stereo Vision SystemStereo Vision allows accurately measuring scenes in 3D – provided that the cameras are well calibrated (camera pose, intrinsic parameters). Especially in harsh industrial or automotive contexts, the mechanical vibrations, temperature variations, and material fatigue affect opto-mechanical properties of the stereo-rig and its measurement accuracy.A jointly developed intelligent door opener (embedded stereo-system: reglomat.bircher.com) can detect, discriminate (humans, vehicles), and track 3D objects in real-time. This enables adequate door openings, e.g. suppressing cross-traffic and accounting for the object’s height to minimize energy loss in cooling storage applications. Because calibration drifts can cause e.g. inappropriate openings/closing, periodic or continuous recalibration of the stereo-rig is advantageous.The goal of this thesis is to explore, test and analyze different methods and algorithms for continuous stereo-vision calibration. The solution has to determine the extrinsic and intrinsic camera parameters without any markers or reference objects. Its performance will be measured mainly in overall accuracy, but also calculation time and memory consumption.
  • AI-based Golf-Coach with Automated Swing Analysis AI-based Golf-Coach with Automated Swing AnalysisDer Golfschwung gilt als einer der komplexesten Bewegungsabläufe aller Sportarten. Für den Amateurgolfer besteht die Schwierigkeit darin, bei einem Fehlschlag den Fehler in seiner Bewegung zu erkennen und entsprechend zu korrigieren. Das Ziel dieser Arbeit ist die automatisierte Analyse des Bewegungsablaufs des Golfspielers anhand von Filmaufnahmen, gezieltes Erkennen fehlerhafter Muster, sowie das Ausgeben von Korrekturvorschlägen.
  • Decentralized Ledger eVoting System Decentralized Ledger eVoting SystemeVoting is an unsolved problem, mainly because of security concerns connected to centralized IT systems. Blockchain technology enables new ways to solve IT problems in a transparent, tamper-proof and decentralized way. However, scalability of such solutions is still unsatisfactory and there’s still no solution that provides privacy of the single voter and transparency of the overall voting process at the same time.This master thesis aims at implementing a voting solution that scales to Swiss national votes. The technologies to be used could include zero knowledge proofs (zk-SNARKS), homomorphic encryption and smart contracts on a public blockchain (i.e. ethereum). Different layer-two technologies shall be evaluated to provide scalability for this use case.

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